The continued popularity of a product often demands that product manufacturers conduct ongoing product improvement. Central to effective product improvement is data on how consumers actually use the product. Various methods exist for attempting to obtain this information. For many products, it is common to employ a group of people, known as a “focus group,” whose members are asked to use the product and provide specific comments to the manufacturer either verbally or in writing. Focus group studies are helpful because they can often be conducted before a product, or an improved version thereof, is released to the general public. The manufacturer can thus consider pre-release refinements to the product. Following a product's release to the public, a manufacturer may also obtain information concerning a product's usage by, for example, monitoring calls to the manufacturer's customer service department. Similarly, the manufacturer can monitor consumer comments from various other sources in an attempt to address such comments in a future version of the product.
Effective product improvement has become particularly important for computer software products to remain competitive. The past twenty years have witnessed an exponential growth in the use of personal computers. Driving this popularity to a large extent has been the availability of computer software that users find appealing. At an early point, software for personal computers was largely character-based and employed a limited number of commands whose use could be generally predicted. Thereafter, personal computer software evolved to the now-familiar graphical user interface, such as that exemplified by the Microsoft Windows operating system products.
The shift to a graphical user interface provided many advantages for the user, such as simplifying the knowledge required to effectively use certain computer software. Graphical user interfaces also offered increased user flexibility regarding use and configuration of the computer. As a result, the permutations of individualized usage of personal computers multiplied. Software manufacturers have an increased need to predict and understand how users actually use a personal computer and the software thereon in order to make product improvements that are meaningful for a broad segment of a user population.
To address this need, computer software manufacturers have employed traditional product usage analysis techniques. For example, often a preliminary, or “beta,” version software is made available to groups of users who use the software and provide comments to the manufacturer. As with products generally, this approach requires a software manufacturer to rely on users' descriptions of software usage. Information can also sometimes be obtained from customer support incidents relating to the software.
While this methodology is helpful in the software area for identifying some pre-release product problems, it does not always provide comprehensive feedback to the manufacturer about how consumers use the software. For example, if a user experiences difficulties with the software and does not communicate these to the manufacturer, the manufacturer can lose potential insights for product improvement. Moreover, if the software contains features that are not used by a significant user population, the manufacturer may have difficulty in learning of such potentially unnecessary features. In addition, it is often difficult for a manufacturer to precisely gauge the spectrum of hardware and telecommunication environments in which the software is actually used. Product capability could be enhanced by better targeting the software to the actual computing environments in which it is used.
In short, the feedback provided to a software manufacturer by traditional product analysis methods has often become too generalized. Particularly with respect to modern computer software, the feedback often fails to provide a comprehensive picture of hardware and software usage and hinders the quick improvement of software to meet users' demands.
As computer hardware and software usage grows, it is becoming increasingly important to obtain up-to-date performance and usage data from a statistically significant population of users. Traditional techniques are becoming less workable, particularly as users of a given software can now number in the tens of million. Moreover, the current approach leaves many informational gaps in communicating how users actually used a product. These limitations are likely to become more significant, particularly as Internet-enabled, embedded computerized devices proliferate, such as microprocessor-equipped home appliances and other common devices.